Search Results for "intel gpu"
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Showing 7 open source projects for "intel gpu"
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Desktop and Mobile Device Management SoftwareDesktop Central is a unified endpoint management (UEM) solution that helps in managing servers, laptops, desktops, smartphones, and tablets from a central location.
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oneDNN
oneAPI Deep Neural Network Library (oneDNN)
This software was previously known as Intel(R) Math Kernel Library for Deep Neural Networks (Intel(R) MKL-DNN) and Deep Neural Network Library (DNNL). oneAPI Deep Neural Network Library (oneDNN) is an open-source cross-platform performance library of basic building blocks for deep learning applications. oneDNN is part of oneAPI. The library is optimized for Intel(R) Architecture Processors, Intel Processor Graphics and Xe Architecture graphics. oneDNN has experimental support for the following architectures: Arm* 64-bit Architecture (AArch64), NVIDIA* GPU, OpenPOWER* Power ISA (PPC64), IBMz* (s390x), and RISC-V. oneDNN is intended for deep learning applications and framework developers interested in improving application performance on Intel CPUs and GPUs. ... -
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waifu2x ncnn Vulkan
waifu2x converter ncnn version, run fast GPU with vulkan
ncnn implementation of waifu2x converter. Runs fast on Intel/AMD/Nvidia/Apple-Silicon with Vulkan API. waifu2x-ncnn-vulkan uses ncnn project as the universal neural network inference framework. -
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AWS Deep Learning Containers
A set of Docker images for training and serving models in TensorFlow
AWS Deep Learning Containers (DLCs) are a set of Docker images for training and serving models in TensorFlow, TensorFlow 2, PyTorch, and MXNet. Deep Learning Containers provide optimized environments with TensorFlow and MXNet, Nvidia CUDA (for GPU instances), and Intel MKL (for CPU instances) libraries and are available in the Amazon Elastic Container Registry (Amazon ECR). The AWS DLCs are used in Amazon SageMaker as the default vehicles for your SageMaker jobs such as training, inference, transforms etc. They've been tested for machine learning workloads on Amazon EC2, Amazon ECS and Amazon EKS services as well. ... -
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SageMaker MXNet Inference Toolkit
Toolkit for allowing inference and serving with MXNet in SageMaker
...AWS Deep Learning Containers (DLCs) are a set of Docker images for training and serving models in TensorFlow, TensorFlow 2, PyTorch, and MXNet. Deep Learning Containers provide optimized environments with TensorFlow and MXNet, Nvidia CUDA (for GPU instances), and Intel MKL (for CPU instances) libraries and are available in the Amazon Elastic Container Registry (Amazon ECR). The AWS DLCs are used in Amazon SageMaker as the default vehicles for your SageMaker jobs such as training, inference, transforms etc. They've been tested for machine learning workloads on Amazon EC2, Amazon ECS and Amazon EKS services as well. -
The Future of Pet Business Software for Modern Pet CareMoeGo is the ultimate partner for your pawsome business to thrive. Use MoeGo for stressless operation, extrodinary customer experience and business growth. Scheduling with MoeGo is a true pleasure. Find the best time slot to book your clients with MoeGo Smart Schedule™. And specially for mobile groomers, optimize your driving route with MoeGo Route™. Set up your online storefront to present your brand, and schedule appointments in seconds. Or extend the capability of your own website with embedding. We collaborate with the best payment infrastructure provider in the industry to ensure fast and secure transactions. Choose any payment method at your convenience, no matter online or in person. Connect with customers outside of your salons and vans. Keep up with your customers’ expectation. Turn every customer into a recurring with industry leading pet parent experience.
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YOLO ROS
YOLO ROS: Real-Time Object Detection for ROS
...Darknet on the CPU is fast (approximately 1.5 seconds on an Intel Core i7-6700HQ CPU @ 2.60GHz × 8) but it's like 500 times faster on GPU! You'll have to have an Nvidia GPU and you'll have to install CUDA. The CMakeLists.txt file automatically detects if you have CUDA installed or not. CUDA is a parallel computing platform and application programming interface (API) model created by Nvidia. -
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TensorFlow MacOS
TensorFlow for macOS 11.0+ accelerated using Apple's ML Compute
This repository provided a pre-release of TensorFlow and TensorFlow Addons tailored for macOS 11+ with native hardware acceleration via Apple’s ML Compute, supporting both Apple Silicon (M1) and Intel Macs. It shipped ready-made Python 3.8 wheels and install scripts so developers could quickly get an accelerated stack running without building from source. As TensorFlow added a Metal PluggableDevice path, the project directed users toward using Apple’s tensorflow-metal to get GPU acceleration on Mac directly through Metal. The README and releases emphasized this was a macOS-optimized distribution, not an upstream fork of TensorFlow, and it cataloged common installation notes and issues encountered by early Apple Silicon users. ... -
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Intel neon
Intel® Nervana™ reference deep learning framework
...Designed for ease of use and extensibility. See the new features in our latest release. We want to highlight that neon v2.0.0+ has been optimized for much better performance on CPUs by enabling Intel Math Kernel Library (MKL). The DNN (Deep Neural Networks) component of MKL that is used by neon is provided free of charge and downloaded automatically as part of the neon installation. The gpu backend is selected by default, so the above command is equivalent to if a compatible GPU resource is found on the system. The Intel Math Kernel Library takes advantages of the parallelization and vectorization capabilities of Intel Xeon and Xeon Phi systems. ...
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